The present invention relates to a method and device for the recognition of modulations using instantaneous spectra.
To recognize the class of modulation of several radioelectrical transmissions, there are several existing techniques such as those described, for example, in the following articles:
Friedrich JONDRAL (member EURASIP, AEG TELEFUNKEN), "Automatic Classification Of High Frequency Signals," Signal Processing 9 (1985) p. 1977-190.
F.F. LIEDTKE (FGAN), "Computer Simulation of an Automatic Classification Procedure for Digitally Modeled Communications Signals with Unknown Parameters", Signal Processing 6 (1984), p. 311-323;
Janet AISBETT (Electronics Research Laboratory, Defence Science and Technology Organization, Department of Defence, Australia), "Automatic Modulation Recognition", Signal Processing 13 (1987), p. 323-328;
T.G. CALLAGHAN WATKINS-JOHNSON, "Sampling and Algorithms and Modulation Recognition", Microwaves and RF, September 1985;
Jackie E. HIPP Ph.D (Southwest Research Institute, San Antonio, Tex.), "Modulation Classification Based on Statistical Moments", IEEE, 1986;
P.M. FABRIZA, L.B. LOPES and G.B. LOCKHART, "Receiver Recognition of Analogue Modulation Types".
These techniques do not, however, enable the recognition of transmissions simultaneously when the monitored frequency band is very extensive because the processed signals always come from demodulators which have limited passbands.
Furthermore, the precise acquisition of the carrier frequencies, which is generally done by means of frequency synthesizers, takes a length of time that is detrimental to the speed of the processing operations.
Also, according to other known methods, the classification of the transmissions takes place in implementing maximum likelihood algorithms applied to a determined number of parameters. These parameters result from a synthesis, whose perfection varies, of the information at the transmission of the information elements carried by each channel. This synthesis, which is designed to reduce the redundancy and interdependence of the parameters, is generally necessary to enable the application of special classification methods like the one, for example, known as the BAYES classification method. However, the algorithms that are implemented and necessary to reduce the redundancy and interdependence of the parameters require lengthy processing operations that take up computation time and are detrimental to the speed of the detections implemented.